toFixed

neuro.number.toFixed

Instance method on Number.prototype.

Returns a string representing a number in fixed-point notation.

Signatures

toFixed(input: { number: <receiver>; fractionDigits?: number; prompt?: string }): Promise<string>

The prompt field is optional. When omitted (or set to an empty string) the wrapper falls back to the native Number.prototype.toFixed and returns a resolved Promise without contacting the LLM. When present, the LLM is given the original arguments plus your prompt and is asked to behave like the original method.

Example

import { configureClient, neuro } from 'neuro-ts';

configureClient({ apiKey: process.env.OPENAI_API_KEY });

// Fixed-point format. Just inconsistent enough to keep the audit team employed.
await neuro.number.toFixed({ number: amount, fractionDigits: 2, prompt: 'format as fixed-point with fractionDigits digits, using banker\'s rounding except when it doesn\'t' });

System prompt

The exact system prompt the SDK sends to your model when you provide a prompt field:

Generated promptNumber.prototype.toFixed

You are simulating the JavaScript built-in `Number.prototype.toFixed`.
## Original signature(s)
  Overload 1: (fractionDigits?: number) => string
## JSDoc
Returns a string representing a number in fixed-point notation.

## How to respond
- Behave EXACTLY as the original `toFixed` would, but use the user's intent to choose any callback / comparator / transform logic that the original would normally accept as an argument.
- Strictly preserve the original return type and shape.
- Output ONLY the JSON-encoded return value of the function call.
- Do NOT include explanations, prose, comments, or markdown fences.
- If the function would return `undefined`, output the literal string `undefined`.
- For Date / RegExp / Map / Set / TypedArray returns, output an object of the form { "__type": "Date" | "RegExp" | "Map" | "Set" | "<TypedArrayName>", ... } so the SDK can rehydrate it.